PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed. 
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Usually one uses PyTorch either as a replacement for numpy to use the power of GPUs, a deep learning research platform that provides maximum flexibility and speed. PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate compute by a huge amount.

PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed. 

PyTorch - Tensors and Dynamic neural networks in Python  with strong GPU acceleration

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions. It supports aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets, High performance merging and joining of data sets, Time series-functionality, Hierarchical axis indexing and lot more. 

Pandas - Powerful Python Data Analysis Toolkit

Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy. Its features include tight integration with NumPy, transparent use of a GPU, dynamic C code generation and lot more.

Theano - Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.

napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy). <BR><BR>
napari supports six main different layer types, Image, Labels, Points, Vectors, Shapes, and Surface, each corresponding to a different data type, visualization, and interactivity. You can add multiple layers of different types into the viewer and then start working with them, adjusting their properties.

napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy).

napari - a fast, interactive, multi-dimensional image viewer for python 

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PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

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napari - a fast, interactive, multi-dimensional image viewer for python

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PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Pandas - Powerful Python Data Analysis Toolkit

Theano - Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.

napari - a fast, interactive, multi-dimensional image viewer for python

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